{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fc8df5b",
   "metadata": {},
   "source": [
    "# OXT - OXTR binding model\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2246c169",
   "metadata": {},
   "source": [
    "Author: Preeti Dubey\n",
    "Title: OXTR complex formation simulations done for myometrial cells when [OXT] = 10 pM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fcefb9d",
   "metadata": {},
   "source": [
    "#### Here, we are defining the ODE model to perform simulation for surface level myometrial cells data provided by lab "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe331f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load packages\n",
    "\n",
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "import matplotlib.pyplot as plt\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b4bebfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OXT and OXTR binding code for wild type and it's standard error\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "\n",
    "\n",
    "def oxtmodel(x, t):\n",
    "    \n",
    "    kon = 6.8e+5  # per molar per min (from Phaneuf paper)\n",
    "    koff = 0.0011 # per min  (from Phaneuf paper)\n",
    "    Av = 6e+23\n",
    "    V = 1.4e-11 # litre It is given as 14047 cubic micro meter \n",
    "    Div = V*Av  # dividend of the oxtr copies \n",
    "    oxt = x[0]\n",
    "    oxtr = x[1]\n",
    "    oxr = x[2]\n",
    "   \n",
    "    \n",
    "    doxtdt = -kon*oxt*(oxtr) + koff*oxr\n",
    "    doxtrdt = -kon*oxt*(oxtr) + koff*oxr\n",
    "    doxrdt = kon*oxt*(oxtr) -koff*oxr\n",
    "\n",
    "    return(doxtrdt, doxtrdt, doxrdt)\n",
    "\n",
    "\n",
    "initial_t = 0\n",
    "end_t = 60*36\n",
    "num = 10000\n",
    "\n",
    "\n",
    "\n",
    "# initial condition for wild type mean, upper bound and lower bound\n",
    "#x0_wt = [1e-8, 2.38678e-9, 0]\n",
    "x0_wt = [1e-11, 1.678e-9, 0]\n",
    "x0_wtub = [1e-11, 1.76e-9, 0]\n",
    "x0_wtlb = [1e-11, 1.6e-9, 0]\n",
    "# initial condition for mutant V281M\n",
    "x0_v281m = [1e-11, 7.4877e-10, 0]\n",
    "x0_v281mub = [1e-11, 8.38e-10, 0]\n",
    "x0_v281mlb = [1e-11, 6.59e-10, 0]\n",
    "# initial condition for mutant P108A\n",
    "x0_p108a = [1e-11, 2.658e-9, 0]\n",
    "x0_p108aub = [1e-11, 2.95e-9, 0]\n",
    "x0_p108alb = [1e-11, 2.36e-9, 0]\n",
    "# initial condition for mutant L206V\n",
    "x0_l206v = [1e-11, 3.044e-9, 0]\n",
    "x0_l206vub = [1e-11, 3.33e-9, 0]\n",
    "x0_l206vlb = [1e-11, 2.76e-9, 0]\n",
    "# initial condition for mutant V45L\n",
    "x0_v45l = [1e-11, 1.96e-9, 0]\n",
    "x0_v45lub = [1e-11, 2.13e-9, 0]\n",
    "x0_v45llb = [1e-11, 1.79e-9, 0]\n",
    "# initial condition for mutant E339K\n",
    "x0_e339k = [1e-11, 1.19e-9, 0]\n",
    "x0_e339kub = [1e-11, 1.25e-9, 0]\n",
    "x0_e339klb = [1e-11, 1.12e-9, 0]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# time span\n",
    "t = np.linspace(initial_t, end_t, num)\n",
    "\n",
    "# ode integration for all types \n",
    "x_wt = odeint(oxtmodel,x0_wt,t)   \n",
    "x_wtub = odeint(oxtmodel,x0_wtub,t)   \n",
    "x_wtlb = odeint(oxtmodel,x0_wtlb,t) \n",
    "\n",
    "x_v281m = odeint(oxtmodel,x0_v281m,t)  \n",
    "x_v281mub = odeint(oxtmodel,x0_v281mub,t)   \n",
    "x_v281mlb = odeint(oxtmodel,x0_v281mlb,t)  \n",
    "\n",
    "x_p108a = odeint(oxtmodel,x0_p108a,t)   \n",
    "x_p108aub = odeint(oxtmodel,x0_p108aub,t)   \n",
    "x_p108alb = odeint(oxtmodel,x0_p108alb,t)\n",
    "\n",
    "x_l206v = odeint(oxtmodel,x0_l206v,t)   \n",
    "x_l206vub = odeint(oxtmodel,x0_l206vub,t)   \n",
    "x_l206vlb = odeint(oxtmodel,x0_l206vlb,t)  \n",
    "\n",
    "x_v45l = odeint(oxtmodel,x0_v45l,t)   \n",
    "x_v45lub = odeint(oxtmodel,x0_v45lub,t)   \n",
    "x_v45llb = odeint(oxtmodel,x0_v45llb,t) \n",
    "\n",
    "x_e339k = odeint(oxtmodel,x0_e339k,t)   \n",
    "x_e339kub = odeint(oxtmodel,x0_e339kub,t)   \n",
    "x_e339klb = odeint(oxtmodel,x0_e339klb,t)   \n",
    "\n",
    "\n",
    "\n",
    "# Volume and avagadro's number \n",
    "Av = 6e+23\n",
    "V = 1.4e-11 # litre It is given as 14047 cubic micro meter \n",
    "Div = V*Av\n",
    "\n",
    "# solution extraction for wild type oxr complex\n",
    "oxt_wt = x_wt[:, 0]\n",
    "oxtr_wt = x_wt[:, 1]\n",
    "oxr_wt = x_wt[:, 2]\n",
    "\n",
    "oxt_wt_c = oxt_wt*Div\n",
    "oxtr_wt_c = oxtr_wt*Div\n",
    "oxr_wt_c = oxr_wt*Div\n",
    "\n",
    "# solution extraction for wt upper bound\n",
    "\n",
    "oxt_wtub = x_wtub[:, 0]\n",
    "oxtr_wtub = x_wtub[:, 1]\n",
    "oxr_wtub = x_wtub[:, 2]\n",
    "\n",
    "oxt_wtub_c = oxt_wtub*Div\n",
    "oxtr_wtub_c = oxtr_wtub*Div\n",
    "oxr_wtub_c = oxr_wtub*Div\n",
    "\n",
    "# solution extraction for wt lower bound\n",
    "\n",
    "oxt_wtlb = x_wtlb[:, 0]\n",
    "oxtr_wtlb = x_wtlb[:, 1]\n",
    "oxr_wtlb = x_wtlb[:, 2]\n",
    "\n",
    "oxt_wtlb_c = oxt_wtlb*Div\n",
    "oxtr_wtlb_c = oxtr_wtlb*Div\n",
    "oxr_wtlb_c = oxr_wtlb*Div\n",
    "\n",
    "# solution extraction for V281M \n",
    "oxt_v281m = x_v281m[:, 0]\n",
    "oxtr_v281m = x_v281m[:, 1]\n",
    "oxr_v281m = x_v281m[:, 2]\n",
    "\n",
    "oxt_v281m_c = oxt_v281m*Div\n",
    "oxtr_v281m_c = oxtr_v281m*Div\n",
    "oxr_v281m_c = oxr_v281m*Div\n",
    "\n",
    "# solution extraction for v281m upper bound\n",
    "\n",
    "oxt_v281mub = x_v281mub[:, 0]\n",
    "oxtr_v281mub = x_v281mub[:, 1]\n",
    "oxr_v281mub = x_v281mub[:, 2]\n",
    "\n",
    "oxt_v281mub_c = oxt_v281mub*Div\n",
    "oxtr_v281mub_c = oxtr_v281mub*Div\n",
    "oxr_v281mub_c = oxr_v281mub*Div\n",
    "\n",
    "# solution extraction for v281m lower bound\n",
    "\n",
    "oxt_v281mlb = x_v281mlb[:, 0]\n",
    "oxtr_v281mlb = x_v281mlb[:, 1]\n",
    "oxr_v281mlb = x_v281mlb[:, 2]\n",
    "\n",
    "oxt_v281mlb_c = oxt_v281mlb*Div\n",
    "oxtr_v281mlb_c = oxtr_v281mlb*Div\n",
    "oxr_v281mlb_c = oxr_v281mlb*Div \n",
    "\n",
    "\n",
    "# solution extraction for P108A \n",
    "oxt_p108a = x_p108a[:, 0]\n",
    "oxtr_p108a = x_p108a[:,1]\n",
    "oxr_p108a = x_p108a[:, 2]\n",
    "\n",
    "oxt_p108a_c = oxt_p108a*Div\n",
    "oxtr_p108a_c = oxtr_p108a*Div\n",
    "oxr_p108a_c = oxr_p108a*Div\n",
    "\n",
    "# solution extraction for p108a upper bound\n",
    "\n",
    "oxt_p108aub = x_p108aub[:, 0]\n",
    "oxtr_p108aub = x_p108aub[:,1]\n",
    "oxr_p108aub = x_p108aub[:, 2]\n",
    "\n",
    "oxt_p108aub_c = oxt_p108aub*Div\n",
    "oxtr_p108aub_c = oxtr_p108aub*Div\n",
    "oxr_p108aub_c = oxr_p108aub*Div\n",
    "\n",
    "# solution extraction for p108a lower bound\n",
    "\n",
    "oxt_p108alb = x_p108alb[:, 0]\n",
    "oxtr_p108alb = x_p108alb[:, 1]\n",
    "oxr_p108alb = x_p108alb[:, 2]\n",
    "\n",
    "oxt_p108alb_c = oxt_p108alb*Div\n",
    "oxtr_p108alb_c = oxtr_p108alb*Div\n",
    "oxr_p108alb_c = oxr_p108alb*Div\n",
    "\n",
    "# solution extraction for L206V \n",
    "oxt_l206v = x_l206v[:, 0]\n",
    "oxtr_l206v = x_l206v[:,1]\n",
    "oxr_l206v = x_l206v[:, 2]\n",
    "\n",
    "oxt_l206v_c = oxt_l206v*Div\n",
    "oxtr_l206v_c = oxtr_l206v*Div\n",
    "oxr_l206v_c = oxr_l206v*Div\n",
    "\n",
    "# solution extraction for l206v upper bound\n",
    "\n",
    "oxt_l206vub = x_l206vub[:, 0]\n",
    "oxtr_l206vub = x_l206vub[:,1]\n",
    "oxr_l206vub = x_l206vub[:, 2]\n",
    "\n",
    "oxt_l206vub_c = oxt_l206vub*Div\n",
    "oxtr_l206vub_c = oxtr_l206vub*Div\n",
    "oxr_l206vub_c = oxr_l206vub*Div\n",
    "\n",
    "# solution extraction for l206v lower bound\n",
    "\n",
    "oxt_l206vlb = x_l206vlb[:, 0]\n",
    "oxtr_l206vlb = x_l206vlb[:, 1]\n",
    "oxr_l206vlb = x_l206vlb[:, 2]\n",
    "\n",
    "oxt_l206vlb_c = oxt_l206vlb*Div\n",
    "oxtr_l206vlb_c = oxtr_l206vlb*Div\n",
    "oxr_l206vlb_c = oxr_l206vlb*Div\n",
    "\n",
    "\n",
    "# solution extraction for V45L \n",
    "oxt_v45l = x_v45l[:, 0]\n",
    "oxtr_v45l = x_v45l[:,1]\n",
    "oxr_v45l = x_v45l[:, 2]\n",
    "\n",
    "oxt_v45l_c = oxt_v45l*Div\n",
    "oxtr_v45l_c = oxtr_v45l*Div\n",
    "oxr_v45l_c = oxr_v45l*Div\n",
    "\n",
    "# solution extraction for v45l upper bound\n",
    "\n",
    "oxt_v45lub = x_v45lub[:, 0]\n",
    "oxtr_v45lub = x_v45lub[:,1]\n",
    "oxr_v45lub = x_v45lub[:, 2]\n",
    "\n",
    "oxt_v45lub_c = oxt_v45lub*Div\n",
    "oxtr_v45lub_c = oxtr_v45lub*Div\n",
    "oxr_v45lub_c = oxr_v45lub*Div\n",
    "\n",
    "# solution extraction for v45l lower bound\n",
    "\n",
    "oxt_v45llb = x_v45llb[:, 0]\n",
    "oxtr_v45llb = x_v45llb[:, 1]\n",
    "oxr_v45llb = x_v45llb[:, 2]\n",
    "\n",
    "oxt_v45llb_c = oxt_v45llb*Div\n",
    "oxtr_v45llb_c = oxtr_v45llb*Div\n",
    "oxr_v45llb_c = oxr_v45llb*Div\n",
    "\n",
    "# solution extraction for E339K \n",
    "oxt_e339k = x_e339k[:, 0]\n",
    "oxtr_e339k = x_e339k[:,1]\n",
    "oxr_e339k = x_e339k[:, 2]\n",
    "\n",
    "oxt_e339k_c = oxt_e339k*Div\n",
    "oxtr_e339k_c = oxtr_e339k*Div\n",
    "oxr_e339k_c = oxr_e339k*Div\n",
    "\n",
    "# solution extraction for e3312k upper bound\n",
    "\n",
    "oxt_e339kub = x_e339kub[:, 0]\n",
    "oxtr_e339kub = x_e339kub[:,1]\n",
    "oxr_e339kub = x_e339kub[:, 2]\n",
    "\n",
    "oxt_e339kub_c = oxt_e339kub*Div\n",
    "oxtr_e339kub_c = oxtr_e339kub*Div\n",
    "oxr_e339kub_c = oxr_e339kub*Div\n",
    "\n",
    "# solution extraction for e3312k lower bound\n",
    "\n",
    "oxt_e339klb = x_e339klb[:, 0]\n",
    "oxtr_e339klb = x_e339klb[:, 1]\n",
    "oxr_e339klb = x_e339klb[:, 2]\n",
    "\n",
    "oxt_e339klb_c = oxt_e339klb*Div\n",
    "oxtr_e339klb_c = oxtr_e339klb*Div\n",
    "oxr_e339klb_c = oxr_e339klb*Div\n",
    "\n",
    "\n",
    "# # Plots of all variants and their standard error (SE)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_figheight(9)\n",
    "fig.set_figwidth(11)\n",
    "fig.subplots_adjust(right=0.5)\n",
    "\n",
    "\n",
    "twin1 = ax.twinx()\n",
    "\n",
    "\n",
    "\n",
    "l1, = ax.plot(t, oxr_l206v/1e-12, ':', linewidth=4, color='blue', label='L206V')\n",
    "l2, = twin1.plot(t, oxr_l206v_c, ':', linewidth=4, color='blue', label='L206V')\n",
    "l3 = ax.fill_between(t, oxr_l206vub/1e-12, oxr_l206vlb/1e-12, color='lightblue', alpha=0.8)\n",
    "p1, = ax.plot(t, oxr_p108a/1e-12, '-.', linewidth=4, color='green', label='P108A')\n",
    "p2, = twin1.plot(t, oxr_p108a_c, '-.', linewidth=4, color='green', label='P108A')\n",
    "p3 = ax.fill_between(t, oxr_p108aub/1e-12, oxr_p108alb/1e-12,color='lightgreen', alpha=0.8)\n",
    "w1, = ax.plot(t, oxr_wt/1e-12, '-', linewidth=4, color='black', label='OxR (WT)')\n",
    "w2, = twin1.plot(t, oxr_wt_c, '-', linewidth=4, color='black', label='Wild-type')\n",
    "w3 = ax.fill_between(t, oxr_wtub/1e-12, oxr_wtlb/1e-12, color='lightgray', alpha=0.8)\n",
    "v41, = ax.plot(t, oxr_v45l/1e-12, '.', linewidth=4, color='purple', label='V45L')\n",
    "v42, = twin1.plot(t, oxr_v45l_c, '.', linewidth=4, color='purple', label='V45L')\n",
    "v43 = ax.fill_between(t, oxr_v45lub/1e-12, oxr_v45llb/1e-12, color='thistle', alpha=0.8)\n",
    "e1,  = ax.plot(t, oxr_e339k/1e-12, 'o', linewidth=4, color='magenta', label='E339K')\n",
    "e2,  = twin1.plot(t, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='E339K')\n",
    "e3 = ax.fill_between(t, oxr_e339kub/1e-12, oxr_e339klb/1e-12, color= 'plum')\n",
    "v1, = ax.plot(t, oxr_v281m/1e-12, '--', linewidth=4, color='red', label='V281M')\n",
    "v2, = twin1.plot(t, oxr_v281m_c, '--', linewidth=4, color='red', label='V281M')\n",
    "v3 =  ax.fill_between(t, oxr_v281mub/1e-12, oxr_v281mlb/1e-12,color='lightpink', alpha=0.8)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"Time (min)\", fontsize=18, fontweight='bold')\n",
    "ax.set_ylabel(\"[OXTR Complex] (pM)\", fontsize=18, fontweight='bold')\n",
    "twin1.set_ylabel(\"[OXTR Complex] (complexes/cell)\", fontsize=18, fontweight='bold')\n",
    "ax.set_xticks(np.arange(0,60*36,350))\n",
    "\n",
    "ax.set_yticks([0,  305,  610,  915, 1220, 1525, 1830, 2135, 2440, 2745, 3050, 3355, 3660, 3965])\n",
    "twin1.set_yticks([0,  2562,  5124,  7686, 10248, 12810, 15372, 17934, 20496, 23058, 25620, 28182, 30744, 33306])\n",
    "\n",
    "tkw = dict(size=4, width=2.5, labelsize=18)\n",
    "ax.tick_params(axis='both',  **tkw)\n",
    "twin1.tick_params(axis='both',  **tkw)\n",
    "\n",
    "# # # Create the inset axis\n",
    "inset1_ax = ax.inset_axes([0.2, 0.2, 0.65, 0.55])\n",
    "insettwin1 = inset1_ax.twinx()\n",
    "\n",
    "#inset1 plot for all \n",
    "\n",
    "l12, = inset1_ax.plot(t, oxr_l206v/1e-12, ':', linewidth=4, color='blue', label='OXTRC (L206V)')\n",
    "lt1, = insettwin1.plot(t, oxr_l206v_c, ':', linewidth=4, color='blue', label='OXTRC (L206V)')\n",
    "lt3 = inset1_ax.fill_between(t, oxr_l206vub/1e-12, oxr_l206vlb/1e-12, color='lightblue', alpha=0.8)\n",
    "p12, = inset1_ax.plot(t, oxr_p108a/1e-12, '-.', linewidth=4, color='green', label='OXTRC (P108A)')\n",
    "pt1, = insettwin1.plot(t, oxr_p108a_c, '-.', linewidth=4, color='green', label='OXTRC (P108A)')\n",
    "\n",
    "pt3 = inset1_ax.fill_between(t, oxr_p108aub/1e-12, oxr_p108alb/1e-12,color='lightgreen', alpha=0.8)\n",
    "\n",
    "w12, = inset1_ax.plot(t, oxr_wt/1e-12, '-', linewidth=4, color='black', label='OXTRC (WT)')\n",
    "wt1, = insettwin1.plot(t, oxr_wt_c, '-', linewidth=4, color='black', label='OXTRC (WT)')\n",
    "wt3 = inset1_ax.fill_between(t, oxr_wtub/1e-12, oxr_wtlb/1e-12, color='lightgray', alpha=0.8)\n",
    "\n",
    "v42, = inset1_ax.plot(t, oxr_v45l/1e-12, '.', linewidth=4, color='purple', label='OXTRC (V45L)')\n",
    "vt42, = insettwin1.plot(t, oxr_v45l_c, '.', linewidth=4, color='purple', label='OXTRC (V45L)')\n",
    "vt43 = inset1_ax.fill_between(t, oxr_v45lub/1e-12, oxr_v45llb/1e-12, color='thistle', alpha=0.8)\n",
    "\n",
    "e12,  = inset1_ax.plot(t, oxr_e339k/1e-12, 'o', linewidth=4, color='magenta', label='OXTRC(E339K)')\n",
    "et1,  = insettwin1.plot(t, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='OXTRC (E339K)')\n",
    "et3 = inset1_ax.fill_between(t, oxr_e339kub/1e-12, oxr_e339klb/1e-12, color= 'plum')\n",
    "\n",
    "v12, = inset1_ax.plot(t, oxr_v281m/1e-12, '--', linewidth=4, color='red', label='OXTRC (V281M)')\n",
    "vt1, = insettwin1.plot(t, oxr_v281m_c, '--', linewidth=4, color='red', label='OXTRC (V281M)')\n",
    "vt3 =  inset1_ax.fill_between(t, oxr_v281mub/1e-12, oxr_v281mlb/1e-12,color='lightpink', alpha=0.8)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# # # Set the limits and formatting of the inset1 axis\n",
    "#inset1_ax.set_xlim(0, 1)\n",
    "inset1_ax.set_ylim(0, 7.2)\n",
    "insettwin1.set_ylim(0, 60.48)\n",
    "inset1_ax.set_xticks(np.arange(0,60*36,700))\n",
    "inset1_ax.set_yticks([0,1,2,3,4,5,6,7])\n",
    "# Set properties for the ticks and tick labels using tkw2\n",
    "tkw2 = dict(size=8, width=2, labelsize=18)\n",
    "inset1_ax.tick_params(axis='both', **tkw2)\n",
    "\n",
    "insettwin1.tick_params(axis='both',  **tkw2)\n",
    "\n",
    "ax.legend(handles=[l1,p1, w1, v41, e1, v1], fontsize=18, loc='upper left', bbox_to_anchor=(1.3, 1.02))\n",
    "plt.savefig(\"oxtrc_myo_oxt_pm_yaxis_pm.jpg\", dpi=400, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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